# Exploring Low-light Object Detection Techniques

arXiv: https://arxiv.org/abs/2107.14382 · HTML: https://mlsanity.com/paper/2107.14382/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 30 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Getting to Know Low-light Images with The Exclusively Dark Dataset](https://mlsanity.com/paper/1805.11227/) (2018)

## Built on by (most cited first, 5 of 5)

- [COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts](https://mlsanity.com/paper/2307.12730/) (2023)
- [ICON2: Reliably Benchmarking Predictive Inequity in Object Detection](https://mlsanity.com/paper/2306.04482/) (2023)
- [MODIPHY: Multimodal Obscured Detection for IoT using PHantom Convolution-Enabled Faster YOLO](https://mlsanity.com/paper/2402.07894/) (2024)
- [Low-light Pedestrian Detection in Visible and Infrared Image Feeds: Issues and Challenges](https://mlsanity.com/paper/2311.08557/) (2023)
- [Indoor scene recognition from images under visual corruptions](https://mlsanity.com/paper/2408.13029/) (2024)

## Abstract (arXiv)

Images acquired by computer vision systems under low light conditions have multiple characteristics like high noise, lousy illumination, reflectance, and bad contrast, which make object detection tasks difficult. Much work has been done to enhance images using various pixel manipulation techniques, as well as deep neural networks - some focused on improving the illumination, while some on reducing the noise. Similarly, considerable research has been done in object detection neural network models. In our work, we break down the problem into two phases: 1)First, we explore which image enhancement algorithm is more suited for object detection tasks, where accurate feature retrieval is more important than good image quality. Specifically, we look at basic histogram equalization techniques and unpaired image translation techniques. 2)In the second phase, we explore different object detection models that can be applied to the enhanced image. We conclude by comparing all results, calculating mean average precisions (mAP), and giving some directions for future work.